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> Gord Pyzer: I've searched for more than4o years to find a winter pike spot that is the equal of the one I've illustrated for this article. Some come close, [...]

You're flying higher than a kid on Christmas morning as you climb into the co-pilot seat of the floatplane that's tied to the dock at a remote northern Canadian air [...]

Conjugated organic photoredox catalysts (OPCs) can promote a wide range of chemical transformations. It is challenging to predict the catalytic activities of OPCs from first principles, either by expert knowledge or by using a priori calculations, as catalyst activity depends on a complex range of interrelated properties. Organic photocatalysts and other catalyst systems have often been discovered by a mixture of design and trial and error. Here we report a two-step data-driven approach to the targeted synthesis of OPCs and the subsequent reaction optimization for metallophotocatalysis, demonstrated for decarboxylative sp3–sp2cross-coupling of amino acids with aryl halides. Our approach uses a Bayesian optimization strategy coupled with encoding of key physical properties using molecular descriptors to identify promising OPCs from a virtual library of 560 candidate molecules. This led to OPC formulations that are competitive with iridium catalysts by exploring just 2.4% of the available catalyst formulation space (107 of 4,500 possible reaction conditions).

If I hadn't seen it with my own eyes and felt the sudden jolt from dozens of huge lake trout reverberate between my shoulder blades, I would never have believed [...]

Pre-baiting an ice-fishing spot to attract walleyes, sauger, lake trout, pike, yellow perch, and whitefish, and chumming your hole while you're fishing, may be the most overlooked facet of the [...]

Most pike waters are complex worlds with considerable diversity, from the aquatic equivalent of lush rainforests to hostile deserts; yet, unlike our world--where landforms and features are relatively static--the environment [...]

I saw magic last autumn when 1 fished with moving-water muskie expert Wally Robins and Southeast Ontario's Ottawa River Muskie Factory owner and Guide John Anderson. They do things to [...]

You've fantasized about it for years, right down to mentally packing your tackle and rigging your rods and now the rubber is hitting the road. Your time has come to [...]

Biobanking during the COVID-19 pandemic presented unique challenges regarding patient enrollment, sample collection, and experimental analysis. This report details the ways in which we rapidly overcame those challenges to create a robust database of clinical information and patient samples while maintaining clinician and researcher safety. We developed a pipeline using REDCap (Research Electronic Data Capture) to coordinate electronic informed consent, sample collection, immunological assay execution, and data analysis for biobanking samples from patients with COVID-19. We then integrated immunological assay data with clinical data extracted from the electronic health record to link study parameters with clinical readouts. Of the 193 inpatients who participated in this study, 138 consented electronically and 56 provided paper consent. We collected and banked blood samples to measure circulating cytokines and chemokines, peripheral immune cell composition and activation status, anti-COVID-19 antibodies, and germline gene polymorphisms. In addition, we collected DNA and RNA from nasopharyngeal swabs to assess viral titer and microbiome composition by 16S sequencing. The rapid spread and contagious nature of COVID-19 required special considerations and innovative solutions to biobank samples quickly while protecting researchers and clinicians. Overall, this workflow and computational pipeline allowed for comprehensive immune profiling of 193 inpatients infected with COVID-19, as well as 89 outpatients, 157 patients receiving curbside COVID-19 testing, and 86 healthy controls. We describe a novel electronic framework for biobanking and analyzing patient samples during COVID-19, and present insights and strategies that can be applied more broadly to other biobank studies.

'Did I hear you correctly?' Duane Kurbis, Arctic Lodges camp manager asked when I suggested visiting northern Saskatchewan's Reindeer Lake during the final week of August. 'Do you really want [...]

It usually pays to be as cunning and crafty as possible when you're fishing for walleye, bass, trout, and other species savvy anglers often drop a marker buoy over the [...]

I believe that what sets In-Fisherman apart from the other magazines is landing on the cutting edge of fishing and being able to look around the corner--spotting the promising new [...]

We Can Catch So many fish species through a hole in the ice--walleyes, yellow perch, black crappies, pike, even smallmouth, largemouth, and catfish--but winter fishing for them isn't prime time. [...]

Some smallmouth bass fisheries are on fire these days--hotter than a green-chile cheeseburger, especially those in the mid- and northern portions of their range. Nowhere is this more apparent than [...]

At the end of the Second World War, Winston Churchill described the Soviet Union as 'a riddle wrapped in a mystery inside an enigma.' Churchill just as easily could have [...]

Anglers across North America are indebted to a handful of distinguished scientists--John Casselman, Peter Colby, John Forney and Dennis Schupp, four of the most important names in walleye history, who [...]

Clinical manifestations of severe COVID-19 include coagulopathies that are exacerbated by the formation of neutrophil extracellular traps (NETs). Here, we report that pulmonary lymphatic vessels, which traffic neutrophils and other immune cells to the lung-draining lymph node (LDLN), can also be blocked by fibrin clots in severe COVID-19. Immunostained tissue sections from COVID-19 decedents revealed widespread lymphatic clotting not only in the lung but also in the LDLN, where the extent of clotting correlated with the presence of abnormal, regressed, or missing germinal centers (GCs). It strongly correlated with the presence of intralymphatic NETs. In mice, tumor necrosis factor α induced intralymphatic fibrin clots; this could be inhibited by DNase I, which degrades NETs. In vitro, TNF-α induced lymphatic endothelial cell upregulation of ICAM-1 and CXCL8, among other neutrophil-recruiting factors, as well as thrombomodulin downregulation; in decedents, lymphatic clotting in LDLNs. In a separate cohort of hospitalized patients, serum levels of Myeloperoxidase-DNA (MPO-DNA, a NET marker) inversely correlated with antiviral antibody titers, but D-dimer levels, indicative of blood thrombosis, did not correlate with either. Patients with high MPO-DNA but low D-dimer levels generated poor antiviral antibody titers. This study introduces lymphatic coagulation in lungs and LDLNs as a clinical manifestation of severe COVID-19 and suggests the involvement of NETosis of lymphatic-trafficking neutrophils. It further suggests that lymphatic clotting may correlate with impaired formation or maintenance of GCs necessary for robust antiviral antibody responses, although further studies are needed to determine whether and how lymphatic coagulation affects adaptive immune responses.

We present a framework that couples a high-fidelity compositional reservoir simulator with Bayesian optimization (BO) for injection well scheduling optimization in geological carbon sequestration. This work represents one of the first at tempts to apply BO and high-fidelity physics models to geological carbon storage. The implicit parallel accurate reservoir simulator (IPARS) is utilized to accurately capture the underlying physical processes during CO2sequestration. IPARS provides a framework for several flow and mechanics models and thus supports both stand-alone and coupled simulations. In this work, we use the compositional flow module to simulate the geological carbon storage process. The compositional flow model, which includes a hysteretic three-phase relative permeability model, accounts for three major CO2trapping mechanisms: structural trapping, residual gas trapping, and solubility trapping. Furthermore, IPARS is coupled to the International Business Machines (IBM) Corporation Bayesian Optimization Accelerator (BOA) for parallel optimizations of CO2injection strategies during field-scale CO2sequestration. BO builds a probabilistic surrogate for the objective function using a Bayesian machine learning algorithm—the Gaussian process regression, and then uses an acquisition function that leverages the uncertainty in the surrogate to decide where to sample. The IBM BOA addresses the three weaknesses of standard BO that limits its scalability in that IBM BOA supports parallel (batch) executions, scales better for high-dimensional problems, and is more robust to initializations. We demonstrate these merits by applying the algorithm in the optimization of the CO2injection schedule in the Cranfield site in Mississippi, USA, using field data. The optimized injection schedule achieves 16% more gas storage volume and 56% less water/surfactant usage compared with the baseline. The performance of BO is compared with that of a genetic algorithm (GA) and a covariance matrix adaptation (CMA)-evolution strategy (ES). The results demonstrate the superior performance of BO, in that it achieves a competitive objective function value with over 60% fewer forward model evaluations.

In molecular discovery and drug design, structure–property relationships and activity landscapes are often qualitatively or quantitatively analyzed to guide the navigation of chemical space. The roughness (or smoothness) of these molecular property landscapes is one of their most studied geometric attributes, as it can characterize the presence of activity cliffs, with rougher landscapes generally expected to pose tougher optimization challenges. Here, we introduce a general, quantitative measure for describing the roughness of molecular property landscapes. The proposed roughness index (ROGI) is loosely inspired by the concept of fractal dimension and strongly correlates with the out-of-sample error achieved by machine learning models on numerous regression tasks.